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For a classification problem, how is the output of the network usually determined?
Say, there are three possible classes, each with a numerical identifier, would a reasonable solution be to sum the outputs and take that sum as the overall output of the network? Or would you take the average of the networks outputs?
There is plenty of information regarding ANN theory, but not much about application, but I apoligise if this is a silly question.
For a multi-layer perceptron classifier with 3 classes, one typically constructs a network with 3 outputs and trains the network so that (1,0,0) is the target output for the first class, (0,1,0) for the second class, and (0,0,1) for the third class. For classifying a new observation, you typically select the output with the greatest value (e.g., (0.12, 0.56, 0.87) would be classified as class 3).